Examining the two-dimensional perceived marketplace influence and the role of financial incentives by SEM and ANN

In recent years, research on sustainable consumption has been particularly relevant, highlighting the importance of the collective over the individual to reduce pollution. This study focuses on the study of the perceived marketplace influence (PMI) concept in its organizational and consumer dimensions, together with the financial incentives that exist in the adoption of electric cars and their effect on green customer engagement. A sample of 382 potential buyers of electric vehicles was obtained. A new hybrid analytical approach was taken structural equation modelling and artificial neural network. The research found the most significant variables affecting purchase intention were financial incentives, followed by PMI Organization and finally PMI Consumer. The results of artificial neural network analysis confirmed all the findings of the structural equation modelling, although the importance of each PMI dimension is different for each technique used. The conclusions point to new business opportunities that can be exploited by companies selling this green technology.Funding for open access charge: Universidad de Granada / CBU

Biometric m-payment systems: A multi-analytical approach to determining use intention

Although mobile payment systems offer countless advantages, they do present certain drawbacks, mainly associated with security and privacy concerns. The inclusion of biometric authentication technologies seeks to minimise such drawbacks. The aim of this article is to examine the effect of key antecedents of the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) and perceived risk on the intention to use a mobile payment system featuring biometric identification. A new hybrid analytical approach is taken. A sample of more than 2500 smartphone users was obtained through an online panel-based survey. Two techniques were used: first, structural equation modelling (PLS-SEM) was conducted to determine which variables had a significant influence on the adoption of the mobile payment system, and second, an artificial neural network (ANN) model was used, taking a deep learning approach, to rank the relative influence of significant predictors of use intention obtained via PLS-SEM. The study found that the most significant variables affecting use intention were performance expectancy, effort expectancy, facilitating conditions, hedonic motivation and risk. In contrast, subjective norms, price value and habit were found to be weak predictors of use intention. The results of the ANN analysis confirmed almost all SEM findings but yielded a slightly different order of influence among the least significant predictors. A review of the extant scientific literature revealed a paucity of published studies dealing with the adoption and use of mobile payment systems featuring biometric identification. The conclusions and managerial implications point to new business opportunities that can be exploited by firms through the use of this technology

ANALYSING THE ROLE OF DRIVER BEHAVIORS IN ROAD TRAFFIC ACCIDENTS: AN APPLICATION OF MACHINE LEARNING

Continuous innovations are taking place worldwide to develop solutions for problems encountered by human beings. The prevention of a variety of accidents due to burning, drowning, terrorism, electric shocks, and road traffic is among the important concerns of researchers and solution developers. Specifically, this current study aims to analyse the contributions of different driver behaviours that resulted in road accidents, followed by proposing a viable solution and reducing the road accident frequencies to benefit society at large. This study employed two methods to analyse data. One was through SEM, and the second was through Artificial Neural Network (ANN). The study is descriptive in nature and it used the survey method to collect sample data from 345 drivers from various professional backgrounds. The questionnaire consisted of independent variables, namely slips, errors, mistakes, lapse violations and unintentional violations. To measure the contributions of these variables towards accidents, age was taken as the moderator. The statistical techniques used included reliability, correlation, and normality analyses, in addition to artificial neural networks and regression analyses. Each factor was found to be a significant contributor to road accidents. Moreover, no significant difference was found in drivers’ behaviour between males and females, but age was found to have a moderating effect on the relationship between driver behaviours and accidents. Additionally, the rate of accidents decreases with the increases in age and vice versa.

Understanding User Perception and Intention to Use Smart Homes for Energy Efficiency: A Survey

The positive impact of Smart Homes on energy efficiency is heavily dependent on how consumers use the system after adoption. While the technical aspects of Smart Home systems and their potential to reduce energy usage is a focus of various studies, there is a limited consideration of behavioral psychology while designing systems for energy management. To investigate users' perception and intention to use Smart Homes to support energy efficiency, we design a research model by combining a theory of planned behavior and the norm activation model. We design a questionnaire and conduct a survey targeting current smart home users (over 350 responses). To analyze the survey results, we extend the partial least squares structural equation modeling (PLS-SEM) by a random forest algorithm. The findings suggest that personal norms have the strongest influence on behavioral intention to use Smart Homes for energy efficiency, followed by the ascription of responsibility. Furthermore, the results support the effects of attitudes, subjective norms, awareness of consequences, as well as the moderating effect of past behavior on the relationship between personal norms and behavioral intentions